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Volume 1 Issue 1 2016
Amity Journal of Finance
1(1), (36-47)
©2016 ADMAA
Dynamics of Price Discovery and Indian Index Futures Market
(A case of S&P CNX NIFTY)
Sheetal Kapoor
Ministry of Parliamentary Affairs, Parliament House, New Delhi, India
(Received: 04/11/2015; Accepted: 31/03/2016)
Abstract
The continuous economic expansion concurrently occurring round the world has given birth to financial
innovations, both in technologies as well as products, one such being derivatives. Derivative contracts saw
light of the day on Indian trading bourses in the year 2000 and ever since they have been associated with
elements like volatility, price discovery etc. The present paper studies price discovery in conjunction with
index futures for the Indian derivative market where S&P CNX NIFTY, the benchmark index of National
Stock Exchange of India Limited and FUTIDX (the futures underlying NIFTY) have been chosen as proxy for
the study. The study deploys Johansen Co-integration test, Granger Causality test to pinpoint the lead lag
relationship between spot and futures market. The time frame of the study stretches from 12th June, 2000 (the
day of introduction of index futures on the S&P CNX NIFTY) to 31st March, 2015. This shows that there exists
a bi-directional relationship between the two set of markets under study. There are also evidences of long
term relationship between the spot index (NIFTY) and futures index (FUTIDX).
Keywords: Co-Integration, Index Futures, Granger Causality, Price Discovery
JEL Classification: C3, C15, C5, G1
Paper Classification: Research Paper
Introduction
The era of globalization and integration of financial markets round the globe has necessitated
the development of new financial instruments capable to deal and counteract increased market
risks. This dire need for risk management paved the way for introduction and use of derivatives
all over the world. With the inception of index futures on the trading bourses, the relationship
between spot price index and index futures have been an area of detailed investigation.
Derivatives tend to provide greater liquidity at lower transaction costs both for hedging and
devising cross-market investment strategies. Specifically, futures contracts were developed as
an instrument of risk transfer and price discovery as they provide a mechanism through which
information about spot and futures prices gets assimilated and thereafter gets disseminated to all
the players in the market mechanism. In common parlance, price discovery is the dynamic process
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by which market prices tend to incorporate new information. The essence of price discovery
function is whether or not new information is reflected in spot or futures market. On theoretical
forefront, the cost-carry model clearly explains the underlying relationship between prices in cash
and futures market. Any departure from the existing equilibrium between the two sets of prices
is automatically corrected either by price in one or both the markets. An efficient market, not only
futures market, tends to generate such prices reflecting consciously formed opinions on cash
prices about the futures, but also transmits the same so as to attain equilibrium, thereby, defining
price discovery.
In India, index futures were introduced as a part of financial market reforms for risk
management in the year 2000 on the recommendations of L.C. Gupta Committee’s Report.
Accordingly, derivatives in the form of index futures were started BSE SENSEX on 9th June, 2000
and on NSE NIFTY on 12th June, 2000; followed by other derivative products like stock futures,
index options etc. Since the time of its inception, the turnover in derivative segment has been
far more promising than expected. Out of the total derivatives traded, almost 99.9% of them are
traded on the derivatives segment at National Stock Exchange of India Limited-there by emerging
as the true representative of derivatives market of the Indian economy. India is emerging as one
among the few successful developing countries, as it provides a vibrant market for exchangetraded derivatives. The turnover of derivative contracts on the bourses of NSE reached to a figure
of Rs.382114.1 billion against Rs.315330 billion rupees and Rs.24 billion in the year bracket of 201213 and 2000-01 respectively. Not only this, the average daily turnover in the equity derivative
segment at National Stock Exchange of India Limited rose from Rs.1266.4 billion in 2012-13 to
Rs.1522.37 billion in 2012-13. Interestingly, the product which had just 21,635,449 contracts to
its credit for the year 2004-05 has seen a mammoth rise and by the end of 2014, the number of
contracts traded rose to 105,270,529, hitting a turnover of 30,852,965 million for the year 2013-14.
The given figure illustrates the turnover of FUTIDX and CNX NIFTY under the period of study:
Figure 1. Turnover of NSE Index Futures vis-à-vis Total Derivatives Turnover
The study of price discovery and information flow across spot and futures market has gained
much attention in the recent past and has been pivot of several empirical literatures. One school
of thought opined that there exists a unidirectional relationship between the futures and spot
and either of the two precedes the other in affecting prices. The other bench of scholars is of
the view that there exists a bi-directional relationship between spot and futures prices. In order
to analyze the same, the present study makes a serious attempt to bring to light the dynamics
of price discovery in the context of Indian economy. The present study is a sincere effort to add
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to the existing literature on price discovery through empirical results. Evidences on the flow of
information across spot and futures market are quite mixed and thus requires unfurling of not
only the nature of relationship but also direction of information flow. There is an examination of
linkages between the market sets and the way in which information is reflected in the futures and
spot prices. Such investigation is pertinent on price discovery mechanism as these have stronger
implications for traders, regulators and practitioners.
Review of Literature
A wide body of research has been devoted to the study of market volatility in conjunction
with the trading of derivatives. But the relationship between spot and futures market has several
other dimensions; one such parameter is price discovery. While some studies have revealed that
spot market serves as a vehicle for price discovery, others have deemed futures market as the
game changer by leading the former as it offers a basket of benefits in the form of low transaction
cost, reduced risk, greater liquidity etc. In fact, the contribution of spot and futures prices have a
great say in the price discovery dynamics; having greater relevance for investors, regulators and
portfolio managers. The present section details out a gist of certain studies in a chronological order
elucidating the increasing recognition of the phenomenon of price discovery.
Earliest study was made by Koch, Kwaller and Kosh (1987) who studied lead lag relationship
between S&P 500 futures and S&P 500 index and remarked that spot prices hardly affect futures
prices rather the corollary is true. Their study deployed Least Squares Regression to comment
upon the minute to minute data on the prices of nearby S&P 500 futures contracts and the S&P
500 index for all the trading days during 1984 and 1985. In the context of Australian stock market,
Frino, Walter and West (2000) studied the price discovery dynamics for a period of five years
between 1992 and 1997 and concluded that a bi-directional set of forces counteract between the
index futures and index returns under the framework of their study. Also, the analysis conducted
on a year-to-year basis suggests that the extent to which the futures market lead the equity market
has decreased over time and the relationship between the two markets has strengthened. In the
recent study, Kim, Kim and Nam (2009) examined the intra-day relationship for the data set of
Korea KOSPI 200 index options and its related spot market. The empirical results revealed that
both the call and put options are efficiently led by the Korean stock market index. Tan (2002)
in his study with respect to Malaysian stock market investigated the causal relationship for
Malaysian stock composite index and the KLFI, i.e., the underlying futures index. Using the
Hsiao’s sequential approach (HSM), Johansen co-integration test, Granger causality test, the
study analyzed both short term and long run price dynamics. The study not only revealed a bidirectional relationship for the short run but there were evidences of co-integration in long run
too. In the context of Indian futures market, the earliest study was that of Raju and Karande (2003)
who analyzed the dynamics of price discovery using Vector Error Correction Model (VECM), Cointegration test for the Bombay Stock Exchange Index and its related futures. Though the study
was based on a shorter time frame it disclosed that price discovery occurs both in spot and futures
market. However, it is the futures market which leads its counterpart. One of the recent studies
of the past decade by Pati and Pradhan (2009) also highlights the price discovery process at NSE.
Pati and Pradhan (2009) in their study for a time span of five years examined the process of price
discovery in context of S&P CNX Nifty Index Futures and its relevant spot market. With the use
of tools like Johansen Co-integration test, Vector Error Correction Model, Variance Decomposition
Analysis, the results pinpoint that short run unidirectional causality exists between futures to spot
market. Their results also advocated the existence of a long run relationship between S&P Nifty
and its related futures.
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Rajput, Kakkar, Batra and Gupta (2012) have examined the phenomenon of price discovery
for a period ranging from January, 2003 to March, 2011. The study utilizes Co-integration test
and VECM Model on the data set of S&P CNX Nifty and the related Index futures contracts.
The results indicated that the futures price exhibit a higher pace of adjustment to the previous
deviations, i.e., there is an existence of a lead lag relationship. To put it in simple terms, the study
identifies a dominant role of Nifty spot which leads the relevant futures market. Kumar and
Chakrapani (2007) conducted a study on the intra-day data set of 46 stocks traded on the bourses
of National Stock Exchange and their respective stock futures during January 2004 to March 2007.
The study identifies the role of single stock futures for the analysis of intra-day price discovery.
Using the Hasbrouck methodology, the study reports a greater role of spot markets in the price
discovery process thereby, dismantling the fact that futures market attract more informed
investors and hence, play a major role in price discovery.
Another study of Kenourgious (2004) reveals the price discovery mechanism of the Athens
futures market where by FTSE/ASE-20 and its futures counterpart is taken up for the study. The
empirical results for the data set of three years pinpoint that futures are an efficient means of
price discovery and attributes a dominant role to the same. Owing to the transparent and secure
functioning in the FTSE/ASE-20 futures market, the futures prices leads the spot prices. The study
uses the VECM and Johnasen co-integration test. The same market was further analyzed using bivariate GARCH in the study of Floros and Vougas (2007). They, however, also included FTS/ASE
40 futures along with the FTSE/ASE-20 futures of the Athens Derivative Exchange. Existence of
a long run relationship was churned out for a period of 2 years. Their results conformed to the
findings of their predecessors and identified futures market as informationally more efficient,
thereby, controlling the price discovery mechanism.
In the study by Debasish (2011), an empirical analysis for the data set of Nifty for a period of 8
years was conducted with respect to futures and options traded on Nifty. The results revealed that
it is the futures market which leads the spot market though the impact degenerates over time. The
study also revealed that the spot market is led more by the options market rather than the futures
market. Apart from these, there have been several studies both in the Indian and international
contexts, which have tried to study and establish the thumb rule of the lead-lag relationship
between the two sets of market. Where initial studies had made use of ARMA model or some form
of bi-variate GARCH, gradually the studies have started using Granger Causality and Vector Error
Correction Model for establishing the relationship. Further, certain studies have also examined
volatility in conjunction with price discovery. This study, however, is an improvement over others
as it is based on the data set of one and a half decade i.e. fifteen years. The study does not lose
its focus by combining several other dimensions associated with derivative contracts. Rather, it
converges on the issue of price discovery specifically. This study is a serious attempt to analyze
the short and long run dynamics of price discovery and aims to enrich the existing state of
literature on empirical grounds.
Contribution of the Study
The present study ventures to unveil the lead lag relationship existing between spot and
futures market. The study covers a total period of fifteen years with respect to near month
contract. The study pinpoints pertinent implications with regard to the role of futures market and
establishes the important link between the two sets of market under study. Finally, the study also
highlights whether or not the futures serve the very purpose of their introduction.
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Research Gap
In the past, there have been several studies with regard to determination of the lead lag
relationship between spot and futures markets. Not only in the market for commodities futures,
there has been ample number of empirical researches in the arena of financial futures as well.
But as a matter of fact, the studies in the past have accounted for empirical findings on basis of
relatively smaller time frame. While the present study stretches over a data set spanning from 12th
June, 2000 (the very first day of commencement of Nifty on the bourses of NSE) to 31st March,
2015 (the recently ended financial year). Owing to such a longer time frame, the results of the
present study have a broader basis and thus account for a relatively greater importance than the
studies with time frame of 3-5 years. The study covers both the short run and long term behavior
pattern between spot and futures market rather than any one of the issue in isolation. The result so
obtained, thus, marks a great relevance for traders, regulatory bodies and practitioners.
Objective of the Study
The study covers the index futures market of National Stock Exchange of India Limited, as
the representation of the entire index futures market of the country. The index futures market
being more than a decade old offers an ample set of data, at hand, to study the dynamics of price
discovery as futures contracts have prima facie role of price discovery. This study proceeds with
the objective of unfurling the fact that whether futures market lead spot market or vice versa. The
study also strives to find out the long term relationship latent between the two set of markets.
Research Methodology
Data of the Study
The study is based on the daily closing prices of both S&P CNX NIFTY and FUTIDX. The data
spans from 12th June, 2000 (the day when index futures were introduced on CNX NIFTY) to 31st
March, 2015. The data for FUTIDX has been extracted for the near month contracts. All the daily
data have been taken from the official website of National Stock Exchange of India Limited.
Hypotheses of the Study
The study proceeds to find the viability of the following sets of hypotheses:
H01 : There exists no short run causal relationship between spot and futures market.
Also,
H02 : Futures prices do not lead spot prices.
Methodology
The study uses daily data of spot and futures prices to detail the relation between the spot and
futures market. The daily log returns is calculated by using the following:
Rt = Log (Pt /Pt-1)
Where
Rt is the daily log return
Pt
is the closing price on day t
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Pt-1 is the closing price on t-1
Where t= 1, 2, 3….. n days
After computation of daily log return, the basic descriptive statistics are calculated to have
an initial insight about the time series data. The Jarque-Bera test rejects the assumption of the
data being normal. Another preliminary exercise for the econometric analysis is the testing for
stationarity and co-integration.
The Augmented-Dickey Fuller test is then deployed to find out whether the series is stationary
i.e., the presence of unit root. As such, the simple unit root test is appropriate only if the series
at hand is of an AR (1) type. But ADF test is deemed for series with higher- order correlation.
Once the series are found to be non-stationery, the series is differentiated for the first difference.
It is indicative of the fact that both futures and spot market may have co-integration between each
other in long run.
Further, Johansen Co-integration test is deployed to find out whether or not co-integration
exists between the spot and futures prices. A long run relationship is indicative of the fact that
the variables move together over time so that disturbances owing to short term relationship are
corrected from the long term trends. The Johansen Co-integration test, as given by Johansen (1988)
helps to determine the number of co-integration equations, as per multi-variate approach.
Moreover, one cannot discard the causal relationship of the short run while studying the
dynamics of price discovery. Granger (1969) gave a bi-variate framework where standard F-test
is set to determine the causal relationship between variables. It is pertinent here to mention that
the lag length determination for both Granger Causality and VAR estimation has been ascertained
using Schwartz Criterion. In order to determine the long run relationship existent between spot
and futures markets, the Vector Error Correction Model is deployed. The following equations
represent the VECM approach:∆Yt= ϑ1 αt-1 + lagged (ΔY,ΔX)+ε1t
∆Xt= ϑ2 αt-1+lagged (ΔY,ΔX)+ε2t
Where
|ϑ1 | + |ϑ2 | ≠ 0
Also,
αt-1 → error lagged one period derived from the co-integrating regression.
Further, Vector Error Correction Model incorporates the lag terms of short run as an
adjustment factor for long run equilibrium. In addition to this, Vector Error Correction Model also
quantifies the relative magnitude of adjustment occurring between the two markets in an attempt
to attain equilibrium. The next section of the study presents the empirical results as per the above
detailed research methodology.
Empirical Results
The present section details the empirical results after different statistical operations are
carried out on the data set. The preliminary exercise of gathering information about the basic
characteristics of the time series is achieved by calculation of descriptive statistics portrayed in
Table 1.
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Table 1: Output of Descriptive Statistics for the period from 12-06-2000 to 31-03-2015
Statistical Measures
NIFTY Returns
FUTIDX Returns
Mean
0.000186
0.0000457
Median
0.000323
0.00142
Maximum
0.291363
0.070333
Minimum
-0.05652
-0.10257
Std. Dev.
0.011541
0.00796
Skewness
11.71992
-0.78625
Kurtosis
301.768
35.00023
Jarque-Bera
5085589
58124.86
Probability
0
0
Sum
0.253222
0.062075
Sum Sq. Dev.
0.18089
0.08605
Observations
1359
1359
For the period under study, the mean for NIFTY returns is 0.000186, a bit greater than the mean
for FUTIDX returns. The measure of Jarque-Bera test is larger enough to defy the assumption of
normality of the data set.
In an efficient market, the activities of either of the two markets (spot and futures) have no
effect on other. But, in real time, such an efficient market structure is far from reality and is only
hypothetical. In such a case, the concept of lead-lag relationship comes into existence when spot
(futures) reacts to the information in futures (spot) market.
The empirical exercise of calculation of descriptive statistics is followed by the AugmentedDickey Fuller test to find out the presence of unit root. The log return series at level turns out to be
non-stationery as t-statistic in both the cases (NIFTY & FUTIDX) are much higher than the critical
values (-3.96482) at 1% level of significance. Both the series are differentiated at first difference
to convert the data into a form of stationery. Applying econometric models on ill-specified series
tend to give spurious results because OLS estimation is not tenable on non-stationery time series
data.
Table 2: Output of ADF test for Nifty Returns (at first difference)
t-Statistic
Probability
ADF test statistic
-35.2519
0
Test critical value 1%
-3.96482
2%
-3.41312
3%
-3.12857
Further, the study proceeds to examine the existence of a long run relationship between the
spot and futures prices. It is pertinent to analyze the long term relationship because it is held
that short term disturbances gets corrected in long run after their shocks are absorbed over
a period of time. The implication of presence of co-integration is that the two markets respond
disproportionately to each other’s prices in short run but tends to get into sync in long run;
provided markets are efficient.
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Table 3: Output of ADF test for FUTIDX Returns (at first difference)
t-Statistic
Probability
ADF test statistic
-37.4539
0
Test critical value 1%
-3.96482
2%
-3.41312
3%
-3.12857
Table 4: Output of Johansen Co-Integration Test
No. of Co-integrating
Equations
Eigen value
Trace Statistic
Critical Value (at 5%)
Probability
None
0.180783
520.343
15.49471
0.0001
At most 1
0.168808
250.3467
3.841466
0
Table 4 provides the results of Johansen Co-integration test. The null hypothesis is rejected thus
pinpointing that there exists two co-integration equations between spot and futures prices. Ahead
of Johansen Co-integration test, the VAR estimation is conducted with a lag length structure as
specified by Schwartz Criterion.
Moreover, the ground for estimating Vector Error Correction Model is that the markets under
study play an important role in price discovery process. Table 5 presents the empirical statistics of
Vector Error Correction Model.
Table 5: Output of Vector Error Correction Model (VECM)
Error Correction:
D(NIFTY_R)
D(FUTIDX_R)
CointEq1
-0.826496
0.275457
D(NIFTY_R(-1))
D(NIFTY_R(-2))
D(FUTIDX_R(-1))
D(FUTIDX_R(-2))
C
-0.0426
-0.03182
[-19.4022]
[ 8.65639]
-0.070544
-0.182563
-0.03565
-0.02663
[-1.97889]
[-6.85569]
-0.076299
-0.106005
-0.02674
-0.01998
[-2.85320]
[-5.30653]
-0.460615
-0.545208
-0.04069
-0.0304
[-11.3187]
[-17.9347]
-0.230302
-0.304128
-0.03502
-0.02616
[-6.57583]
[-11.6247]
9.34E-06
-6.53E-06
-0.00032
-0.00024
[ 0.02911]
[-0.02724]
`
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Table 6: Output of Granger Causality Test
Null Hypothesis
Observations
F-Statistic
Probability
1357
0.12298
0.008843
1.69104
0.1847
NIFTY returns do not Granger Cause FUTIDX returns
FUTIDX returns do not Granger Cause NIFTY returns
After estimating the presence and nature of long run relationship between the spot and futures
prices, the study deploys Granger Causality test to check and comment on the short run causality
between the two market forms. It is found that in short run, it is the spot market of NIFTY which
leads/influences the futures market, FUTIDX.
Discussion
The purpose of this research has been examination of price discovery between the benchmark
index of National Stock Exchange of India (i.e. S&P CNX Nifty) and the corresponding futures
market. The justification for the introduction of index and stock futures was to minimize the
level of risk and provide an efficient mechanism of price discovery, thereby, improving the
market efficiency. However, the empirical findings suggest that it is not the futures market which
contributes towards price discovery. Rather, the spot market leads the futures market. The results
so obtained conforms the existence of long run relationship between the two sets of market, but
when it comes to influencing the price discovery mechanism, the spot prices comes to the forefront
and hence play a dominant, role i.e., information is first reflected in spot prices. The result of the
present study is in line with the results of Thenmozhi and Thomas (2007); Kumar and Chakrapani
(2007); Koch, Kwaller and Kosh (1987).
These results conform to the line of thought where changes in spot prices are attributed to
trigger action on the part of different market participants, thereby, causing subsequent changes
in the futures price. However, in context of Indian derivatives market, the studies in the past have
revealed different indications. Studies of Raju and Karande (2003); and Pati and Pradhan (2009)
are in sharp contrast to the present study in terms of impounding. This can be attributed to shorter
time frame considered in these studies or the market so chosen (Bombay Stock Exchange is the
market under study in the former case).
Conclusion
The study has examined the causality and price discovery for spot and futures prices in the
case of S&P CNX NIFTY. The Augmented-Dickey Fuller (ADF) test has reported both the series
to be non-stationery at level and hence at first differentiation, the series are turned to be one of
stationery. The empirical results of Granger-Causality test indicate that the spot prices do affect
the futures prices and hence the spot market leads the futures market in the short run. A h e a d
of this, the output of Johansen co-integration test suggests the presence of long run relationship
dynamics between spot and futures prices. Further, the Vector Error Correction Model comes
out with the results of price discovery by revealing that spot prices play a dominant role in price
discovery. It is because prices of spot market tend to discover and assimilate new information
faster than futures prices, i.e., spot market serves as an efficient channel of price discovery. It is,
therefore, evident that spot market leads futures market. This can be traced to the amateur market
for futures which is not as old as spot market and thus needs time to grow and contribute in the
process of price discovery. The spot market in India is more actively traded and is thus, upheld in
contrast to the market for futures. The results reveal that futures market make greater adjustments
in order to re-establish the equilibrium and thus, pinpoint that spot market leads the futures
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market. No doubt, the study is based on daily closing data rather than high frequency data but still
it is sufficient enough in elucidating that price discovery does occur in spot market of NIFTY index
and that the spot and its counterpart are co-integrated to an extent.
Such evidences of price discovery in spot market is of great use to regulators as they can
develop new policies to enable futures market to grow in a way so as to contribute in faster
dissemination of new information leading to price discovery.
Limitations of the Study
The present study tracks the relationship existing between spot and futures market during a
period of fifteen years. The study has not considered the time intervals while obtaining empirical
outputs. It could be possible that owing to several financial developments, there could be different
price discovery phenomenon prevalent, i.e., a set of financial data summarizes within itself
a great set of changes both in market forces and policy decision. It may happen that if the data
is clubbed into sets of three years on the basis of certain parameters, each period may exhibit
different pattern. In other words, price discovery process in one market may exhibit “spot leading
the futures price” while the other may take a reverse pattern (probability of such indication is
very less). Also, the results are based on daily log returns. However, minute to minute data may
be capable of altering the direction of lead-lag relationship to some extent of not significantly.
However, the study reveals the lead lag relationship in the best possible way for a time frame of
fifteen years and is well sufficed with a larger number of observations.
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Author’s Profile
Sheetal Kapoor obtained her doctorate in the area of derivatives from Banaras Hindu University,
Varanasi, India. She has to her credit fifteen research papers in international and national journals and has
presented papers at several conferences at national and international levels. Her research interest includes
stock market phenomenon, derivatives, merchant banking etc.
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